Basketball Data Scientist

As a Basketball Data Scientist at Swish Analytics, you will focus on developing and improving machine learning models tailored for sports betting products. Your responsibilities will include creating feature sets using domain-specific knowledge, engaging in the full model development lifecycle, and collaborating with data engineering and product teams. Additionally, you'll conduct rigorous testing and analysis to enhance model performance and articulate your findings to both technical and non-technical stakeholders.

To qualify for the Basketball Data Scientist role at Swish Analytics, you should ideally hold a master’s degree in Data Analytics, Data Science, Computer Science, or a related technical field; a PhD is preferred. You need a minimum of four years of experience in developing production-scale models in sports betting, along with proficiency in Python, SQL, and tools like GitHub. Knowledge of advanced statistical methods and strong leadership qualities are also essential.

The essential skills for a Basketball Data Scientist at Swish Analytics include expertise in machine learning and statistical modeling, a solid foundation in Probability Theory, and knowledge of Bayesian statistics. You should also possess excellent problem-solving abilities, the capability to work in AWS environments, and proficiency in coding with Python and SQL. Strong communication skills, enabling you to effectively present complex data insights to various stakeholders, are equally important.

The salary range for a Basketball Data Scientist at Swish Analytics falls between $120,000 and $175,000. This range accommodates different experience levels, ensuring that candidates with various backgrounds can find a fit. Your specific compensation will depend on your skills and experience, so there's room for negotiation based on your background.

At Swish Analytics, Basketball Data Scientists have exceptional opportunities for career advancement. As a crucial part of our data team, you will play a significant role in influencing product development and strategic decision-making. With demonstrated success and strong leadership skills, you'll have the chance to take on larger projects, mentor new team members, and even move into managerial roles within the analytics department.

When answering this question, be specific about the model's architecture, the data used, and the performance metrics that you measured. Discuss the algorithms you chose and why they were appropriate for your sports betting application. Present the challenges faced during development and how you addressed them, emphasizing the impact of your work on business outcomes.

To ensure data quality, emphasize that you start with a thorough understanding of the data sources. Discuss methods such as data cleaning, validation techniques, and how you handle missing data. Additionally, emphasize any automated systems you may have implemented for ongoing data quality checks to maintain high standards throughout your modeling process.

In your response, highlight your strategies, whether using automated techniques like recursive feature elimination or employing domain knowledge to justify selected variables. Discuss how features impact model accuracy and your process for iteratively refining selections based on model performance metrics.

When discussing a challenging problem, provide a detailed narrative that showcases your problem-solving skills. Articulate the problem, the solution you implemented, the thought process involved, and the eventual outcome. Highlight what you learned from the experience and how it influenced your approach to future challenges.

In your answer, mention subscribing to industry publications, attending conferences, and participating in online forums. Discuss any relevant courses you've taken to enhance your skill set, and express your commitment to lifelong learning to stay ahead in the rapidly evolving field of data analytics.

Provide a specific instance where you had to simplify complex data insights for a broader audience. Talk about the techniques you used, such as visualizations or analogies, and the feedback you received. Emphasize the importance of making data accessible to all stakeholders to drive informed decision-making.

Share your preferred programming languages, libraries, and any analytics tools you've found to be particularly effective. Emphasize your versatility but highlight your favorites, detailing scenarios where each tool was especially beneficial. Mention keeping updated with new technologies as well.

Frame your answer around a collaborative project where you played a significant role. Describe the team members involved, your contributions, and the outcome of the effort. Emphasize the importance of communication and teamwork in achieving successful data-driven solutions.

Outline the key performance metrics you assess, such as accuracy, precision, recall, and AUC-ROC. Discuss the importance of not only considering these metrics in isolation but also in context with business outcomes while iteratively testing and refining models to improve their performance.

Express your enthusiasm for the position by connecting it to your passion for basketball and data science. Discuss what excites you about Swish's innovative approach to sports analytics and how your skills and career aspirations align with the company's mission. Showcase your eagerness to contribute to the team while also growing professionally.